Quantile Estimation Using Ranked Set Samples from a Population with Known Mean
Year
: 2012
Abstract: Ranked set sampling (RSS) is a cost-efficient technique for data collection when the
units in a population can be easily judgment ranked by any cheap method other
than actual measurements. Using auxiliary information in developing statistical
procedures for inference about different population characteristics is a well-known
approach. In this work, we deal with quantile estimation from a population with
known mean when data are obtained according to RSS scheme. Through the
simple device of mean-correction (subtract off the sample mean and add on the
known population mean), a modified estimator is constructed from the standard
quantile estimator. Asymptotic normality of the new estimator and its asymptotic
efficiency relative to the original estimator are derived. Simulation results for several
underlying distributions show that the proposed estimator is more efficient than the
traditional one.
units in a population can be easily judgment ranked by any cheap method other
than actual measurements. Using auxiliary information in developing statistical
procedures for inference about different population characteristics is a well-known
approach. In this work, we deal with quantile estimation from a population with
known mean when data are obtained according to RSS scheme. Through the
simple device of mean-correction (subtract off the sample mean and add on the
known population mean), a modified estimator is constructed from the standard
quantile estimator. Asymptotic normality of the new estimator and its asymptotic
efficiency relative to the original estimator are derived. Simulation results for several
underlying distributions show that the proposed estimator is more efficient than the
traditional one.
Keyword(s): Mean-correction,Quantile estimation,Ranked set sampling
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Statistics
Quantile Estimation Using Ranked Set Samples from a Population with Known Mean
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| contributor author | مهدی مهدی زاده | en |
| contributor author | ناصررضا ارقامی | en |
| contributor author | Mahdi Mahdizadeh | fa |
| contributor author | Nasser Reza Arghami | fa |
| date accessioned | 2020-06-06T13:15:15Z | |
| date available | 2020-06-06T13:15:15Z | |
| date issued | 2012 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3347740?locale-attribute=en | |
| description abstract | Ranked set sampling (RSS) is a cost-efficient technique for data collection when the units in a population can be easily judgment ranked by any cheap method other than actual measurements. Using auxiliary information in developing statistical procedures for inference about different population characteristics is a well-known approach. In this work, we deal with quantile estimation from a population with known mean when data are obtained according to RSS scheme. Through the simple device of mean-correction (subtract off the sample mean and add on the known population mean), a modified estimator is constructed from the standard quantile estimator. Asymptotic normality of the new estimator and its asymptotic efficiency relative to the original estimator are derived. Simulation results for several underlying distributions show that the proposed estimator is more efficient than the traditional one. | en |
| language | English | |
| title | Quantile Estimation Using Ranked Set Samples from a Population with Known Mean | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | Mean-correction | en |
| subject keywords | Quantile estimation | en |
| subject keywords | Ranked set sampling | en |
| journal title | Communications in Statistics Part B: Simulation and Computation | fa |
| pages | 1872-1881 | |
| journal volume | 41 | |
| journal issue | 10 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1037551.html | |
| identifier articleid | 1037551 |